Why You Need a Data Scientist on Your Filmmaking Team

Published on in Advice / Tips & Tricks

In Hollywood, filmmakers assume huge risks when making blockbuster movies. If a film performs poorly at the box office, it can lead to big losses and write-offs. These professionals leverage hundreds of millions of dollars for marketing, production and other movie making related expenses.

For film executives, it's important to minimize their studios’ exposure to failure. As a result, today’s movie executives use advanced technology, such as big data systems, to understand how the public might respond to pending works. For example, predictive analytics technology helps studio heads forecast the likelihood that a film will succeed.

What Is a Data Scientist?

Data scientists leverage disciplines such as multivariable calculus and linear algebra to unearth small opportunities for improvement. These professionals are well versed in filmmaking as well as skills such as analytical problem-solving and communication.

In filmmaking and many other industries, there’s a growing shortage of skilled data scientists. In fact, predict career analysts, the demand for data scientists will double this year.

Increasingly, modern studios are employing data scientists who are highly-skilled in analytics. The insights that they unearth using data systems impact the decision-making of Hollywood's top executives. IBM, for instance, provides big data analytics systems for top Hollywood filmmakers. Using IBM’s Social Sentiment Index, film executives deploy natural language processing and complex algorithms to measure test audiences’ emotional responses to films.

Data Analytics in Filmmaking

Tinseltown executives must be able to predict audience behavior and make relatively accurate marketing decisions. As a result, they use sophisticated big data systems to develop micro-targeted advertisements and marketing campaigns, as well as deliver the right trailer content to the right audiences. The Chief Analytics Officer of Legendary Entertainment, for instance, leverages predictive analytics and artificial intelligence (AI) technologies to optimize production outcomes. Using these resources, the studio has found success with films such as “Jurassic World” and “Straight Out of Compton.”

Technologies such as AI, machine learning and predictive analytics are game changers in the world of business. In the filmmaking realm, however, data scientists must sometimes develop customized software solutions for work in their industry. From customer service to marketing operations, big data systems are helping enterprise leaders optimize existing processes and develop new ones. These kinds of resources augment the innate skills of today’s media executives.

Data Scientists Help Control Media

Studio executives must stay informed about their brand’s online status, public relations and consumer engagement. Resultantly, data scientists monitor and review their enterprise’s online activities, while using big data systems to evaluate public sentiment.

The i-Safe foundation reveals that 50-percent of all youth are subject to cyberbullying or have engaged in this harmful practice. Accordingly, modern-day data science professionals also help studios deal with potentially harmful issues such as negative publicity and cyberbullying. As these kinds of threats become more commonplace, forward-thinking enterprises are increasingly deploying incident response (IR) teams to deal with these undesirable and detrimental events.

Computer security incident response teams (CSIRTs) respond to negative events as they occur. This might involve developing an incident response plan (IRP), investigating trolling and cyberbullying incidents and managing organizational engagement with the public. CSIRT reports encapsulate negative incidents so that they can be easily shared with enterprise stakeholders. Typically, the CSIRT team works with a community emergency response team (CERT) and the security operations Center (SOC), and in some extreme exceptions, data scientists may share their reports industrywide.

Producing a Hollywood Hit Is Harder Than You Think

There are many elements involved in production and filmmaking. In fact, the actual filming of a movie is only a small part of the process. Many professionals collaborate to create great films.

To clarify how important data analysis is for filmmaking, Tomorrowland for instance — which cost the Walt Disney Company $150 million to produce — was a massive failure. This highlights the importance of using big data technology for risk mitigation as a vital organizational process that’s required before green-lighting films.

Data analytics tools help movie executives identify positive and negative public sentiments as well as the reasons behind those feelings. For example, a lower positive sentiment could be the result of a bad reaction to a movie scene, rather than general disinterest. With this kind of reporting, film executives have helped promote a positive sentiment for successful films such as DreamWorks “Puss in Boots."

Additionally, Hollywood execs need to know how audiences react to different parts of a film. This helps them to forecast whether the film will be an overall success. To make such forecasts, Hollywood business leaders deploy technologies such as predictive analytics. In fact, the Harvard Business Review reported that predictive analytics is an essential component for producing profitable films.

Modern film studios can benefit greatly from the ability to predict the success of their productions. Using big data technology, Hollywood producers can analyze many variables to determine the probability of a film’s success and keep audiences entertained and satisfied for years to come.

ProductionHUB ProductionHUB Logo

About the Author

Ryan Ayers
Ryan Ayers
Ryan Ayers has consulted a number of Fortune 500 companies within multiple industries including information technology and big data. After earning his MBA in 2010, Ayers also began working with start-up companies and aspiring entrepreneurs, with a keen focus on data collection and analysis.

Related Blog Posts


There are no comments on this blog post.

You must be logged in to leave a comment.